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Pengcheng Jing

2 accepted papers

2026

Building a Foundational Guardrail for General Agentic Systems via Synthetic Data

ICLR 2026poster

While LLM agents can plan multi-step tasks, intervening at the planning stage—before any action is executed—is often the safest way to prevent harm, since certain risks can lead to severe consequences once carried out. However, existing guardrails mostly operate post-execution, which is difficult to…

Cited by 0SourcecodeScholar
2026

DRIFT-BENCH: Diagnosing CoopeRative Breakdowns in LLM Agents under Input Faults via Multi-Turn Interaction

ICML 2026poster

As Large Language Models transition to autonomous agents, user inputs frequently violate cooperative assumptions (e.g., implicit intent, missing parameters, false presuppositions, or ambiguous expressions), creating execution risks that text-only evaluations do not capture. Existing benchmarks typic…

Cited by 0SourceScholar